Measuring the Directionality of Coupling: Phase Versus State Space Dynamics and Application to Eeg Time Series

Osterhage, Hannes; Mormann, Florian; Wagner, Tobias; Lehnertz, Klaus

doi:10.1142/s0129065707001019

Cited by 61 publications

(38 citation statements)

References 19 publications

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“…48 Changes in spectral-coherence characteristics were revealed at all frequency bands, with maximal changes at 9 frequencies in drug addicts and in narrow-frequency a subranges in alcoholics. Ethanol and heroin consumers were characterized by different effects in the ¡3 band (19)(20)(21). Polysubstance abusers have been associated with reduced interhemispheric 5 and 0 bands, and frontal ¡3 band coherence.…”

Section: ~47mentioning

confidence: 99%

Drug Polyconsumption Is Associated With Increased Synchronization of Brain Electrical-Activity at Rest and in a Counting Task

Coullaut-Valera¹,

Arbaiza²,

Bajo

et al. 2013

Int. J. Neur. Syst.

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Drug abusers typically consume not just one but several types of drugs, starting from alcohol and marijuana consumption, and then dramatically lapsing into addiction to harder drugs, such as cocaine, heroin, or amphetamine. The brain of drug abusers presents various structural and neurophysiological abnormalities, some of which may predate drug consumption onset. However, how these changes translate into modifications in functional brain connectivity is still poorly understood. To characterize functional connectivity patterns, we recorded Electroencephalogram (EEG) activity from 21 detoxified drug abusers and 20 age-matched control subjects performing a simple counting task and at rest activity. To evaluate the cortical brain connectivity network we applied the Synchronization Likelihood algorithm. The results showed that drug abusers had higher synchronization levels at low frequencies, mainly in the 0 band (4-8 Hz) between frontal and posterior cortical regions. During the counting task, patients showed increased synchronization in the ¡3 (14-35Hz), and 7 (35-45Hz) frequency bands, in fronto-posterior and interhemispheric temporal regions. Taken together 'slow-down' at rest and task-related 'over-exertion' could indicate that the brain of drug abusers is suffering from a premature form of ageing. Future studies will clarify whether this condition can be reversed following prolonged periods of abstinence.

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Section: ~47mentioning

confidence: 99%

Drug Polyconsumption Is Associated With Increased Synchronization of Brain Electrical-Activity at Rest and in a Counting Task

Coullaut-Valera¹,

Arbaiza²,

Bajo

et al. 2013

Int. J. Neur. Syst.

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“…This phase-modeling approach is based on the concept of phase synchronization ) between weakly coupled oscillators and was previously applied to intracranial EEG time series by Osterhage et al (2007). First, we extracted unwrapped phases from the preselected and prefiltered iEEG trials (4096 ms length) using wavelet transforms based on Morlet wavelets.…”

Section: Analysesmentioning

confidence: 99%

Interactions between Medial Temporal Lobe, Prefrontal Cortex, and Inferior Temporal Regions during Visual Working Memory: A Combined Intracranial EEG and Functional Magnetic Resonance Imaging Study

Axmacher¹,

Schmitz²,

Wagner³

et al. 2008

J. Neurosci.

179

131

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It is a fundamental question whether the medial temporal lobe (MTL) supports only long-term memory encoding, or contributes to working memory (WM) processes as well. Recent data suggest that the MTL is activated whenever multiple items or item features are being maintained in WM. This may rely on interactions between the MTL or the prefrontal cortex (PFC) and content-specific areas in the inferior temporal (IT) cortex. Here, we investigated the neural mechanism through which the MTL, PFC, and IT cortex interact during WM maintenance. First, we quantified phase synchronization of intracranial EEG data in epilepsy patients with electrodes in both regions. Second, we used directional coupling analysis to study whether oscillatory activity in the IT cortex drives the MTL or vice versa. Finally, we investigated functional connectivity in functional magnetic resonance imaging data of healthy subjects with seeds in the MTL and PFC. With increasing load, EEG phase synchronization between the IT cortex and anterior parahippocampal gyrus and within the MTL increased. Coupling was bidirectional in all load conditions, but changed toward an increased top-down (anterior parahippocampal gyrus 3 IT) coupling in the high gamma range (51-75 Hz) with increasing load. Functional connectivity between the MTL seed and the visual association cortex increased with load, but activity within the MTL and the PFC correlated with fewer voxels, suggesting that more specific neural networks were engaged. These data indicate that WM for multiple items depends on an increased strength of top-down control of activity within the IT cortex by the MTL.

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“…As a case study they apply these measures to EEG signals taken from one epilepsy patient during a seizure-free interval. 13 Adeli et al have shown that the wavelet transform was particularly effective for representing various aspects of non-stationary signals.…”

Section: -12mentioning

confidence: 99%

Automatic Identification of Epileptic and Background Eeg Signals Using Frequency Domain Parameters

Faust

Acharya

Min

et al. 2010

Int. J. Neur. Syst.

152

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The analysis of electroencephalograms continues to be a problem due to our limited understanding of the signal origin. This limited understanding leads to ill-defined models, which in turn make it hard to design effective evaluation methods. Despite these shortcomings, electroencephalogram analysis is a valuable tool in the evaluation of neurological disorders and the evaluation of overall cerebral activity. We compared different model based power spectral density estimation methods and different classification methods. Specifically, we used the autoregressive moving average as well as from Yule-Walker and Burg's methods, to extract the power density spectrum from representative signal samples. Local maxima and minima were detected from these spectra. In this paper, the locations of these extrema are used as input to different classifiers. The three classifiers we used were: Gaussian mixture model, artificial neural network, and support vector machine. The classification results are documented with confusion matrices and compared with receiver operating characteristic curves. We found that Burg's method for spectrum estimation together with a support vector machine classifier yields the best classification results. This combination reaches a classification rate of 93.33%, the sensitivity is 98.33% and the specificy is 96.67%.

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Measuring the Directionality of Coupling: Phase Versus State Space Dynamics and Application to Eeg Time Series

Cited by 61 publications

References 19 publications

Drug Polyconsumption Is Associated With Increased Synchronization of Brain Electrical-Activity at Rest and in a Counting Task

Drug Polyconsumption Is Associated With Increased Synchronization of Brain Electrical-Activity at Rest and in a Counting Task

Interactions between Medial Temporal Lobe, Prefrontal Cortex, and Inferior Temporal Regions during Visual Working Memory: A Combined Intracranial EEG and Functional Magnetic Resonance Imaging Study

Automatic Identification of Epileptic and Background Eeg Signals Using Frequency Domain Parameters

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